Enterprise & partnerships

If you're in industry or government and looking to access the technology, research or education expertise at Monash University, make us your first point of call. Visit Enterprise and partnerships for more.

Synopsis

After a review of basic probability and random processes, the use of stochastic models for real world signals is illustrated. A family of algorithms for the creation, efficient representation and effective modelling is presented.

Specifically, linear stochastic models are presented and the importance of correlation structure in deriving the parameters of such models is illustrated.

The unit also covers how parametric and non-parametric models as well as statistical techniques are used to extract information from data signals corrupted by noise. The concept of estimation from real world data is presented, as opposed to the basic analysis of signals, transfer functions and power spectra. In particular, the fundamentals of linear estimation theory and optimal filtering to design advanced signal processing algorithms are presented.

Outcomes

On successful completion of this unit, students will be able to:

describe various models for real world signals

analyse the performance of a range of estimation methods

simulate a wide range of stochastic signal processing algorithms and interpret the results

design specific algorithms for processing real world signals such as audio, financial data and biomedical data.

Assessment

Continuous assessment: 50%

Examination: (2 hours) 50%

Students are required to achieve at least 45% in the total continuous assessment component (assignments, tests, mid-semester exams, laboratory reports) and at least 45% in the final examination component and an overall mark of 50% to achieve a pass grade in the unit. Students failing to achieve this requirement will be given a maximum of 45% in the unit.